In this paper, we propose and evaluate a method for optimizing descriptors used for content-based multimedia indexing and retrieval. A large variety of descriptors are commonly used for this purpose. However, the most efficient ones often have characteristics preventing them to be easily used in large scale systems. They may have very high dimensionality (up to tens of thousands dimensions) and/or be suited for a distance costly to compute (e.g. χ 2). The proposed method combines a PCA-based dimensionality reduction with pre-and post-PCA non-linear transformations. The resulting transformation is globally optimized. The produced descriptors have a much lower dimensionality while performing at least as well, and often significantly better, with the Euclidean distance than the original high dimensionality descriptors with their optimal distance. The method has been validated and evaluated for a variety of descriptors using TRECVid 2010 semantic indexing task data. It has then be applied at large scale for the TRECVid 2012 semantic indexing task on tens of descriptors of various types and with initial dimensionalities from 15 up to 32,768. The same transformation can be used also for multimedia retrieval in the context of query by example and/or relevance feedback.
This paper presents an audio-visual data representation for violent scenes detection in movies. Existing works in this field consider either the audio or the visual information; or their shallow fusion. None has yet explored their joint dependence for violent scenes detection. We propose a feature which provides strong multi-modal audio and visual cues by first joining the audio and the visual features and then revealing statistically the joint multi-modal patterns. Experimental validation was conducted in the context of the Violent Scenes Detection task of the MediaEval 2013 Multimedia benchmark. The obtained results show the potential of the proposed approach in comparison to methods using audio and visual features separately and other fusion methods.
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